Overview

Dataset statistics

Number of variables23
Number of observations38984
Missing cells0
Missing cells (%)0.0%
Duplicate rows5517
Duplicate rows (%)14.2%
Total size in memory6.8 MiB
Average record size in memory184.0 B

Variable types

Numeric19
Categorical4

Alerts

Dataset has 5517 (14.2%) duplicate rowsDuplicates
ALT is highly overall correlated with AST and 1 other fieldsHigh correlation
AST is highly overall correlated with ALTHigh correlation
Cholesterol is highly overall correlated with LDLHigh correlation
Gtp is highly overall correlated with ALTHigh correlation
LDL is highly overall correlated with CholesterolHigh correlation
age is highly overall correlated with height(cm)High correlation
eyesight(left) is highly overall correlated with eyesight(right)High correlation
eyesight(right) is highly overall correlated with eyesight(left)High correlation
hearing(left) is highly overall correlated with hearing(right)High correlation
hearing(right) is highly overall correlated with hearing(left)High correlation
height(cm) is highly overall correlated with age and 2 other fieldsHigh correlation
hemoglobin is highly overall correlated with height(cm) and 1 other fieldsHigh correlation
relaxation is highly overall correlated with systolicHigh correlation
systolic is highly overall correlated with relaxationHigh correlation
waist(cm) is highly overall correlated with weight(kg)High correlation
weight(kg) is highly overall correlated with height(cm) and 2 other fieldsHigh correlation
hearing(left) is highly imbalanced (82.9%)Imbalance
hearing(right) is highly imbalanced (82.5%)Imbalance
AST is highly skewed (γ1 = 24.00016422)Skewed
ALT is highly skewed (γ1 = 36.16930567)Skewed

Reproduction

Analysis started2025-12-15 12:09:27.818549
Analysis finished2025-12-15 12:10:15.823063
Duration48 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.127591
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:15.898298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q140
median40
Q355
95-th percentile65
Maximum85
Range65
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.063564
Coefficient of variation (CV)0.27337916
Kurtosis-0.1354517
Mean44.127591
Median Absolute Deviation (MAD)10
Skewness0.27809333
Sum1720270
Variance145.52957
MonotonicityNot monotonic
2025-12-15T12:10:15.986085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4010667
27.4%
454946
12.7%
604262
 
10.9%
503901
 
10.0%
553451
 
8.9%
353148
 
8.1%
302868
 
7.4%
252459
 
6.3%
201133
 
2.9%
65928
 
2.4%
Other values (4)1221
 
3.1%
ValueCountFrequency (%)
201133
 
2.9%
252459
 
6.3%
302868
 
7.4%
353148
 
8.1%
4010667
27.4%
454946
12.7%
503901
 
10.0%
553451
 
8.9%
604262
 
10.9%
65928
 
2.4%
ValueCountFrequency (%)
8514
 
< 0.1%
80197
 
0.5%
75424
 
1.1%
70586
 
1.5%
65928
 
2.4%
604262
 
10.9%
553451
 
8.9%
503901
 
10.0%
454946
12.7%
4010667
27.4%

height(cm)
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.68949
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:16.638431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile150
Q1160
median165
Q3170
95-th percentile180
Maximum190
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1875072
Coefficient of variation (CV)0.055786846
Kurtosis-0.5994114
Mean164.68949
Median Absolute Deviation (MAD)5
Skewness-0.14443756
Sum6420255
Variance84.410288
MonotonicityNot monotonic
2025-12-15T12:10:16.721067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1707985
20.5%
1657004
18.0%
1606236
16.0%
1755617
14.4%
1555292
13.6%
1503132
 
8.0%
1802203
 
5.7%
145843
 
2.2%
185495
 
1.3%
140147
 
0.4%
Other values (3)30
 
0.1%
ValueCountFrequency (%)
1301
 
< 0.1%
1354
 
< 0.1%
140147
 
0.4%
145843
 
2.2%
1503132
 
8.0%
1555292
13.6%
1606236
16.0%
1657004
18.0%
1707985
20.5%
1755617
14.4%
ValueCountFrequency (%)
19025
 
0.1%
185495
 
1.3%
1802203
 
5.7%
1755617
14.4%
1707985
20.5%
1657004
18.0%
1606236
16.0%
1555292
13.6%
1503132
 
8.0%
145843
 
2.2%

weight(kg)
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.938718
Minimum30
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:16.813958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile45
Q155
median65
Q375
95-th percentile90
Maximum135
Range105
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.896581
Coefficient of variation (CV)0.19558434
Kurtosis0.32395635
Mean65.938718
Median Absolute Deviation (MAD)10
Skewness0.54482139
Sum2570555
Variance166.3218
MonotonicityNot monotonic
2025-12-15T12:10:16.917624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
655733
14.7%
605671
14.5%
705413
13.9%
555120
13.1%
754241
10.9%
503872
9.9%
802871
7.4%
851775
 
4.6%
451661
 
4.3%
901051
 
2.7%
Other values (12)1576
 
4.0%
ValueCountFrequency (%)
305
 
< 0.1%
3530
 
0.1%
40324
 
0.8%
451661
 
4.3%
503872
9.9%
555120
13.1%
605671
14.5%
655733
14.7%
705413
13.9%
754241
10.9%
ValueCountFrequency (%)
1351
 
< 0.1%
1304
 
< 0.1%
1255
 
< 0.1%
12017
 
< 0.1%
11537
 
0.1%
11082
 
0.2%
105145
 
0.4%
100325
 
0.8%
95601
1.5%
901051
2.7%

waist(cm)
Real number (ℝ)

High correlation 

Distinct545
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.062115
Minimum51
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:17.031430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile67
Q176
median82
Q388
95-th percentile98
Maximum129
Range78
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.326798
Coefficient of variation (CV)0.11365534
Kurtosis0.16646696
Mean82.062115
Median Absolute Deviation (MAD)6
Skewness0.2620206
Sum3199109.5
Variance86.98916
MonotonicityNot monotonic
2025-12-15T12:10:17.181984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801350
 
3.5%
821260
 
3.2%
811227
 
3.1%
841200
 
3.1%
781153
 
3.0%
861141
 
2.9%
851108
 
2.8%
831099
 
2.8%
791082
 
2.8%
761044
 
2.7%
Other values (535)27320
70.1%
ValueCountFrequency (%)
512
 
< 0.1%
542
 
< 0.1%
552
 
< 0.1%
564
< 0.1%
56.21
 
< 0.1%
56.61
 
< 0.1%
578
< 0.1%
57.42
 
< 0.1%
57.51
 
< 0.1%
57.71
 
< 0.1%
ValueCountFrequency (%)
1291
 
< 0.1%
1281
 
< 0.1%
127.71
 
< 0.1%
1272
< 0.1%
125.81
 
< 0.1%
1242
< 0.1%
1231
 
< 0.1%
121.41
 
< 0.1%
1213
< 0.1%
120.51
 
< 0.1%

eyesight(left)
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0149549
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:17.297066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.49852722
Coefficient of variation (CV)0.49118167
Kurtosis178.77825
Mean1.0149549
Median Absolute Deviation (MAD)0.2
Skewness10.107092
Sum39567
Variance0.24852939
MonotonicityNot monotonic
2025-12-15T12:10:17.395160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1.28880
22.8%
18615
22.1%
1.55524
14.2%
0.83594
9.2%
0.93591
9.2%
0.73116
 
8.0%
0.61777
 
4.6%
0.51472
 
3.8%
0.4860
 
2.2%
0.3609
 
1.6%
Other values (9)946
 
2.4%
ValueCountFrequency (%)
0.1248
 
0.6%
0.2315
 
0.8%
0.3609
 
1.6%
0.4860
 
2.2%
0.51472
 
3.8%
0.61777
 
4.6%
0.73116
 
8.0%
0.83594
9.2%
0.93591
9.2%
18615
22.1%
ValueCountFrequency (%)
9.970
 
0.2%
2286
 
0.7%
1.91
 
< 0.1%
1.81
 
< 0.1%
1.614
 
< 0.1%
1.55524
14.2%
1.39
 
< 0.1%
1.28880
22.8%
1.12
 
< 0.1%
18615
22.1%

eyesight(right)
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0087677
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:17.488221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.49381271
Coefficient of variation (CV)0.48952074
Kurtosis180.94817
Mean1.0087677
Median Absolute Deviation (MAD)0.2
Skewness10.117849
Sum39325.8
Variance0.24385099
MonotonicityNot monotonic
2025-12-15T12:10:17.575627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1.28819
22.6%
18730
22.4%
1.55306
13.6%
0.83726
9.6%
0.93688
9.5%
0.72990
 
7.7%
0.61699
 
4.4%
0.51530
 
3.9%
0.4937
 
2.4%
0.3587
 
1.5%
Other values (7)972
 
2.5%
ValueCountFrequency (%)
0.1264
 
0.7%
0.2357
 
0.9%
0.3587
 
1.5%
0.4937
 
2.4%
0.51530
 
3.9%
0.61699
 
4.4%
0.72990
 
7.7%
0.83726
9.6%
0.93688
9.5%
18730
22.4%
ValueCountFrequency (%)
9.968
 
0.2%
2261
 
0.7%
1.615
 
< 0.1%
1.55306
13.6%
1.35
 
< 0.1%
1.28819
22.6%
1.12
 
< 0.1%
18730
22.4%
0.93688
9.5%
0.83726
9.6%

hearing(left)
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size304.7 KiB
1
37995 
2
 
989

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38984
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
137995
97.5%
2989
 
2.5%

Length

2025-12-15T12:10:17.677272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T12:10:17.754391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
137995
97.5%
2989
 
2.5%

Most occurring characters

ValueCountFrequency (%)
137995
97.5%
2989
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
137995
97.5%
2989
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
137995
97.5%
2989
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
137995
97.5%
2989
 
2.5%

hearing(right)
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size304.7 KiB
1
37963 
2
 
1021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38984
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
137963
97.4%
21021
 
2.6%

Length

2025-12-15T12:10:17.832851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T12:10:17.897718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
137963
97.4%
21021
 
2.6%

Most occurring characters

ValueCountFrequency (%)
137963
97.4%
21021
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
137963
97.4%
21021
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
137963
97.4%
21021
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
137963
97.4%
21021
 
2.6%

systolic
Real number (ℝ)

High correlation 

Distinct125
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.47563
Minimum71
Maximum233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:17.980161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile100
Q1112
median120
Q3130
95-th percentile144
Maximum233
Range162
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.643521
Coefficient of variation (CV)0.11231488
Kurtosis1.2681719
Mean121.47563
Median Absolute Deviation (MAD)10
Skewness0.45871864
Sum4735606
Variance186.14568
MonotonicityNot monotonic
2025-12-15T12:10:18.106116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1102476
 
6.4%
1202407
 
6.2%
1302332
 
6.0%
1182099
 
5.4%
1161099
 
2.8%
1241087
 
2.8%
1191060
 
2.7%
1281058
 
2.7%
1221023
 
2.6%
1001015
 
2.6%
Other values (115)23328
59.8%
ValueCountFrequency (%)
711
 
< 0.1%
721
 
< 0.1%
741
 
< 0.1%
791
 
< 0.1%
802
 
< 0.1%
814
< 0.1%
826
< 0.1%
835
< 0.1%
845
< 0.1%
853
< 0.1%
ValueCountFrequency (%)
2331
 
< 0.1%
2231
 
< 0.1%
2131
 
< 0.1%
2081
 
< 0.1%
2041
 
< 0.1%
2033
< 0.1%
2002
< 0.1%
1994
< 0.1%
1982
< 0.1%
1972
< 0.1%

relaxation
Real number (ℝ)

High correlation 

Distinct94
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.994408
Minimum40
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:18.253949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60
Q170
median76
Q382
95-th percentile91
Maximum146
Range106
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.6587342
Coefficient of variation (CV)0.12709796
Kurtosis1.0651649
Mean75.994408
Median Absolute Deviation (MAD)6
Skewness0.41183046
Sum2962566
Variance93.291147
MonotonicityNot monotonic
2025-12-15T12:10:18.407671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
803819
 
9.8%
703658
 
9.4%
782257
 
5.8%
721515
 
3.9%
601491
 
3.8%
741486
 
3.8%
761450
 
3.7%
751336
 
3.4%
821259
 
3.2%
841168
 
3.0%
Other values (84)19545
50.1%
ValueCountFrequency (%)
402
 
< 0.1%
421
 
< 0.1%
442
 
< 0.1%
451
 
< 0.1%
464
 
< 0.1%
471
 
< 0.1%
488
 
< 0.1%
494
 
< 0.1%
5027
0.1%
5140
0.1%
ValueCountFrequency (%)
1463
< 0.1%
1401
 
< 0.1%
1371
 
< 0.1%
1362
< 0.1%
1331
 
< 0.1%
1322
< 0.1%
1302
< 0.1%
1291
 
< 0.1%
1282
< 0.1%
1271
 
< 0.1%

fasting blood sugar
Real number (ℝ)

Distinct258
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.342269
Minimum46
Maximum423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:18.607774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile80
Q189
median96
Q3104
95-th percentile130
Maximum423
Range377
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.642741
Coefficient of variation (CV)0.20779414
Kurtosis32.454561
Mean99.342269
Median Absolute Deviation (MAD)7
Skewness4.3239736
Sum3872759
Variance426.12277
MonotonicityNot monotonic
2025-12-15T12:10:18.809581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
941552
 
4.0%
951524
 
3.9%
971520
 
3.9%
911489
 
3.8%
931487
 
3.8%
921464
 
3.8%
901386
 
3.6%
961379
 
3.5%
981370
 
3.5%
991364
 
3.5%
Other values (248)24449
62.7%
ValueCountFrequency (%)
462
 
< 0.1%
481
 
< 0.1%
511
 
< 0.1%
541
 
< 0.1%
551
 
< 0.1%
562
 
< 0.1%
572
 
< 0.1%
581
 
< 0.1%
594
< 0.1%
607
< 0.1%
ValueCountFrequency (%)
4231
 
< 0.1%
3981
 
< 0.1%
3911
 
< 0.1%
3861
 
< 0.1%
3752
< 0.1%
3693
< 0.1%
3651
 
< 0.1%
3491
 
< 0.1%
3422
< 0.1%
3411
 
< 0.1%

Cholesterol
Real number (ℝ)

High correlation 

Distinct279
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.88349
Minimum55
Maximum445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:19.011423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile141
Q1172
median195
Q3219
95-th percentile259
Maximum445
Range390
Interquartile range (IQR)47

Descriptive statistics

Standard deviation36.353945
Coefficient of variation (CV)0.18464699
Kurtosis0.6791754
Mean196.88349
Median Absolute Deviation (MAD)24
Skewness0.41753917
Sum7675306
Variance1321.6093
MonotonicityNot monotonic
2025-12-15T12:10:19.224694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199479
 
1.2%
192470
 
1.2%
178462
 
1.2%
198458
 
1.2%
187456
 
1.2%
186449
 
1.2%
197445
 
1.1%
188445
 
1.1%
179440
 
1.1%
196440
 
1.1%
Other values (269)34440
88.3%
ValueCountFrequency (%)
551
 
< 0.1%
772
 
< 0.1%
841
 
< 0.1%
861
 
< 0.1%
871
 
< 0.1%
902
 
< 0.1%
915
< 0.1%
934
< 0.1%
953
< 0.1%
962
 
< 0.1%
ValueCountFrequency (%)
4451
< 0.1%
4421
< 0.1%
4411
< 0.1%
4191
< 0.1%
4101
< 0.1%
4061
< 0.1%
3951
< 0.1%
3932
< 0.1%
3861
< 0.1%
3802
< 0.1%

triglyceride
Real number (ℝ)

Distinct389
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.74946
Minimum8
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:19.437468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q174
median108
Q3160
95-th percentile278
Maximum999
Range991
Interquartile range (IQR)86

Descriptive statistics

Standard deviation71.803143
Coefficient of variation (CV)0.56649663
Kurtosis2.0977678
Mean126.74946
Median Absolute Deviation (MAD)39
Skewness1.3366262
Sum4941201
Variance5155.6913
MonotonicityNot monotonic
2025-12-15T12:10:19.631890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71373
 
1.0%
82364
 
0.9%
85364
 
0.9%
79353
 
0.9%
83332
 
0.9%
68327
 
0.8%
67326
 
0.8%
69325
 
0.8%
59322
 
0.8%
75320
 
0.8%
Other values (379)35578
91.3%
ValueCountFrequency (%)
81
 
< 0.1%
111
 
< 0.1%
151
 
< 0.1%
163
 
< 0.1%
191
 
< 0.1%
206
< 0.1%
216
< 0.1%
224
< 0.1%
237
< 0.1%
249
< 0.1%
ValueCountFrequency (%)
9991
 
< 0.1%
5481
 
< 0.1%
4662
 
< 0.1%
4322
 
< 0.1%
39914
< 0.1%
3988
< 0.1%
39716
< 0.1%
3965
 
< 0.1%
3958
< 0.1%
39411
< 0.1%

HDL
Real number (ℝ)

Distinct122
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.293146
Minimum4
Maximum359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:19.821065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile37
Q147
median55
Q366
95-th percentile84
Maximum359
Range355
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.617822
Coefficient of variation (CV)0.25514086
Kurtosis5.9524953
Mean57.293146
Median Absolute Deviation (MAD)9
Skewness1.0869945
Sum2233516
Variance213.68073
MonotonicityNot monotonic
2025-12-15T12:10:20.008714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
501183
 
3.0%
511158
 
3.0%
551155
 
3.0%
541154
 
3.0%
471144
 
2.9%
481113
 
2.9%
521113
 
2.9%
561112
 
2.9%
491110
 
2.8%
531110
 
2.8%
Other values (112)27632
70.9%
ValueCountFrequency (%)
41
 
< 0.1%
183
 
< 0.1%
211
 
< 0.1%
223
 
< 0.1%
233
 
< 0.1%
249
 
< 0.1%
2513
 
< 0.1%
2613
 
< 0.1%
2721
0.1%
2837
0.1%
ValueCountFrequency (%)
3591
 
< 0.1%
1591
 
< 0.1%
1571
 
< 0.1%
1551
 
< 0.1%
1482
< 0.1%
1441
 
< 0.1%
1362
< 0.1%
1352
< 0.1%
1334
< 0.1%
1321
 
< 0.1%

LDL
Real number (ℝ)

High correlation 

Distinct286
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.08149
Minimum1
Maximum1860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:20.203988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile63
Q191
median113
Q3136
95-th percentile172
Maximum1860
Range1859
Interquartile range (IQR)45

Descriptive statistics

Standard deviation42.883163
Coefficient of variation (CV)0.37263301
Kurtosis369.14081
Mean115.08149
Median Absolute Deviation (MAD)22
Skewness11.764551
Sum4486337
Variance1838.9657
MonotonicityNot monotonic
2025-12-15T12:10:20.416137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112526
 
1.3%
110507
 
1.3%
107497
 
1.3%
106496
 
1.3%
101482
 
1.2%
108480
 
1.2%
116480
 
1.2%
121479
 
1.2%
111471
 
1.2%
114470
 
1.2%
Other values (276)34096
87.5%
ValueCountFrequency (%)
13
< 0.1%
41
 
< 0.1%
72
< 0.1%
91
 
< 0.1%
102
< 0.1%
124
< 0.1%
131
 
< 0.1%
153
< 0.1%
164
< 0.1%
172
< 0.1%
ValueCountFrequency (%)
18601
< 0.1%
18101
< 0.1%
16601
< 0.1%
16001
< 0.1%
15601
< 0.1%
14001
< 0.1%
13401
< 0.1%
12601
< 0.1%
12201
< 0.1%
12002
< 0.1%

hemoglobin
Real number (ℝ)

High correlation 

Distinct143
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.624264
Minimum4.9
Maximum21.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:20.632416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile12.1
Q113.6
median14.8
Q315.8
95-th percentile16.9
Maximum21.1
Range16.2
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.5665276
Coefficient of variation (CV)0.10711839
Kurtosis1.2543531
Mean14.624264
Median Absolute Deviation (MAD)1.1
Skewness-0.6674485
Sum570112.3
Variance2.4540086
MonotonicityNot monotonic
2025-12-15T12:10:20.848383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.41080
 
2.8%
15.61054
 
2.7%
151049
 
2.7%
15.31035
 
2.7%
15.71023
 
2.6%
15.51022
 
2.6%
15.2979
 
2.5%
15.8975
 
2.5%
15.1972
 
2.5%
14.9971
 
2.5%
Other values (133)28824
73.9%
ValueCountFrequency (%)
4.91
 
< 0.1%
51
 
< 0.1%
5.51
 
< 0.1%
5.82
< 0.1%
5.91
 
< 0.1%
6.11
 
< 0.1%
6.21
 
< 0.1%
6.32
< 0.1%
6.41
 
< 0.1%
6.63
< 0.1%
ValueCountFrequency (%)
21.11
 
< 0.1%
20.91
 
< 0.1%
20.41
 
< 0.1%
201
 
< 0.1%
19.81
 
< 0.1%
19.72
< 0.1%
19.61
 
< 0.1%
19.34
< 0.1%
19.22
< 0.1%
19.13
< 0.1%

Urine protein
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0865227
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:20.965734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40210688
Coefficient of variation (CV)0.37008604
Kurtosis35.700906
Mean1.0865227
Median Absolute Deviation (MAD)0
Skewness5.5877426
Sum42357
Variance0.16168994
MonotonicityNot monotonic
2025-12-15T12:10:21.051063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
136836
94.5%
21236
 
3.2%
3667
 
1.7%
4182
 
0.5%
558
 
0.1%
65
 
< 0.1%
ValueCountFrequency (%)
136836
94.5%
21236
 
3.2%
3667
 
1.7%
4182
 
0.5%
558
 
0.1%
65
 
< 0.1%
ValueCountFrequency (%)
65
 
< 0.1%
558
 
0.1%
4182
 
0.5%
3667
 
1.7%
21236
 
3.2%
136836
94.5%

serum creatinine
Real number (ℝ)

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88603017
Minimum0.1
Maximum11.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:21.147654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.8
median0.9
Q31
95-th percentile1.2
Maximum11.6
Range11.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.22062147
Coefficient of variation (CV)0.24899995
Kurtosis348.85269
Mean0.88603017
Median Absolute Deviation (MAD)0.1
Skewness9.0256833
Sum34541
Variance0.048673833
MonotonicityNot monotonic
2025-12-15T12:10:21.258469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.97963
20.4%
0.87363
18.9%
16806
17.5%
0.75234
13.4%
1.14264
10.9%
0.63137
 
8.0%
1.22028
 
5.2%
0.51022
 
2.6%
1.3632
 
1.6%
1.4211
 
0.5%
Other values (24)324
 
0.8%
ValueCountFrequency (%)
0.115
 
< 0.1%
0.22
 
< 0.1%
0.37
 
< 0.1%
0.4143
 
0.4%
0.51022
 
2.6%
0.63137
 
8.0%
0.75234
13.4%
0.87363
18.9%
0.97963
20.4%
16806
17.5%
ValueCountFrequency (%)
11.61
< 0.1%
101
< 0.1%
9.91
< 0.1%
7.42
< 0.1%
6.41
< 0.1%
5.91
< 0.1%
51
< 0.1%
3.42
< 0.1%
3.31
< 0.1%
31
< 0.1%

AST
Real number (ℝ)

High correlation  Skewed 

Distinct195
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.198235
Minimum6
Maximum1090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:21.383651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15
Q119
median23
Q329
95-th percentile46
Maximum1090
Range1084
Interquartile range (IQR)10

Descriptive statistics

Standard deviation19.175595
Coefficient of variation (CV)0.73194226
Kurtosis1048.4498
Mean26.198235
Median Absolute Deviation (MAD)4
Skewness24.000164
Sum1021312
Variance367.70346
MonotonicityNot monotonic
2025-12-15T12:10:21.529848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202636
 
6.8%
212604
 
6.7%
222501
 
6.4%
192474
 
6.3%
232334
 
6.0%
182292
 
5.9%
242237
 
5.7%
251936
 
5.0%
171897
 
4.9%
261593
 
4.1%
Other values (185)16480
42.3%
ValueCountFrequency (%)
61
 
< 0.1%
72
 
< 0.1%
82
 
< 0.1%
916
 
< 0.1%
1034
 
0.1%
1172
 
0.2%
12202
 
0.5%
13383
 
1.0%
14676
1.7%
151122
2.9%
ValueCountFrequency (%)
10902
< 0.1%
9811
< 0.1%
9761
< 0.1%
8411
< 0.1%
7781
< 0.1%
6561
< 0.1%
5912
< 0.1%
5271
< 0.1%
3871
< 0.1%
3261
< 0.1%

ALT
Real number (ℝ)

High correlation  Skewed 

Distinct230
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.145188
Minimum1
Maximum2914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:21.651234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median21
Q331
95-th percentile62
Maximum2914
Range2913
Interquartile range (IQR)16

Descriptive statistics

Standard deviation31.309945
Coefficient of variation (CV)1.1534252
Kurtosis2545.5193
Mean27.145188
Median Absolute Deviation (MAD)7
Skewness36.169306
Sum1058228
Variance980.31266
MonotonicityNot monotonic
2025-12-15T12:10:21.784361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151927
 
4.9%
161863
 
4.8%
171802
 
4.6%
181791
 
4.6%
141781
 
4.6%
191625
 
4.2%
131620
 
4.2%
201545
 
4.0%
121511
 
3.9%
211426
 
3.7%
Other values (220)22093
56.7%
ValueCountFrequency (%)
11
 
< 0.1%
21
 
< 0.1%
33
 
< 0.1%
415
 
< 0.1%
531
 
0.1%
681
 
0.2%
7188
 
0.5%
8362
 
0.9%
9577
1.5%
10947
2.4%
ValueCountFrequency (%)
29141
< 0.1%
17831
< 0.1%
16121
< 0.1%
14002
< 0.1%
11551
< 0.1%
7452
< 0.1%
7401
< 0.1%
7131
< 0.1%
6101
< 0.1%
5141
< 0.1%

Gtp
Real number (ℝ)

High correlation 

Distinct439
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.905038
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size304.7 KiB
2025-12-15T12:10:21.909166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q117
median26
Q344
95-th percentile113
Maximum999
Range997
Interquartile range (IQR)27

Descriptive statistics

Standard deviation49.693843
Coefficient of variation (CV)1.2453025
Kurtosis77.179393
Mean39.905038
Median Absolute Deviation (MAD)11
Skewness6.7754457
Sum1555658
Variance2469.478
MonotonicityNot monotonic
2025-12-15T12:10:22.037513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161484
 
3.8%
141477
 
3.8%
151454
 
3.7%
171400
 
3.6%
181387
 
3.6%
131312
 
3.4%
191288
 
3.3%
201239
 
3.2%
211168
 
3.0%
121086
 
2.8%
Other values (429)25689
65.9%
ValueCountFrequency (%)
21
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
59
 
< 0.1%
630
 
0.1%
773
 
0.2%
8162
 
0.4%
9367
 
0.9%
10636
1.6%
11931
2.4%
ValueCountFrequency (%)
9994
< 0.1%
9761
 
< 0.1%
9612
< 0.1%
9331
 
< 0.1%
9261
 
< 0.1%
8941
 
< 0.1%
8731
 
< 0.1%
8361
 
< 0.1%
8201
 
< 0.1%
8162
< 0.1%

dental caries
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size304.7 KiB
0
30625 
1
8359 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38984
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
030625
78.6%
18359
 
21.4%

Length

2025-12-15T12:10:22.152717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T12:10:22.218977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
030625
78.6%
18359
 
21.4%

Most occurring characters

ValueCountFrequency (%)
030625
78.6%
18359
 
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
030625
78.6%
18359
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
030625
78.6%
18359
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
030625
78.6%
18359
 
21.4%

smoking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size304.7 KiB
0
24666 
1
14318 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38984
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024666
63.3%
114318
36.7%

Length

2025-12-15T12:10:22.300224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T12:10:22.365286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
024666
63.3%
114318
36.7%

Most occurring characters

ValueCountFrequency (%)
024666
63.3%
114318
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
024666
63.3%
114318
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
024666
63.3%
114318
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
024666
63.3%
114318
36.7%

Interactions

2025-12-15T12:10:13.236910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:31.712990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.877677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:36.053216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:38.036067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:40.453366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:43.267436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:45.543917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:47.735132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:50.053416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:52.172642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:54.907607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:57.580425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:59.667191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:01.791532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:03.906110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:06.649354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:09.208284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:11.181833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:13.349960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:31.829605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.981069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:36.160436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:38.143590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:40.557216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:43.369701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:45.663324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:47.855301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:50.161667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:52.289201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:55.059581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:57.686101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:59.774938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:01.899881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:04.009259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:06.798911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:09.313429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:11.294707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:13.461941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:31.931389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:34.084487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:36.280760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:38.258037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:40.720147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:43.470850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:45.792249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:47.959463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:50.274796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:52.402008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:55.225387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:57.798028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:59.881785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:02.009795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:04.112346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:06.947523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:09.423970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:11.403446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:13.564591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:32.036395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:34.202885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:38.381826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:40.866709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:43.574452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:45.897487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:48.055456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:50.379992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:52.508050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:55.385238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:57.904154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:59.984094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:02.117470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:04.211246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:07.089530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:09.522575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:11.507096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:13.673562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:32.151881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:50.493519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:55.557110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:58.013425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:00.103368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:10:07.242957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:09.627346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:11.614847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:13.775119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:32.249732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:36.575745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:38.808625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:48.271185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:52.738869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:10:09.729174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:11.720966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:13.873638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:32.493190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:37.094941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:39.407223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:41.949287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:44.604590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:46.710525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:48.800512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:51.166761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:53.434508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:56.275630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:58.689099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:00.791562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:02.914931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:04.968518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:08.195725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:10.264422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:12.265146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:14.435710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.022521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:35.045600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:37.198625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:39.522011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:42.090737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:44.723749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:46.838727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:48.913846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:51.276776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:53.595491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:56.372993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:58.794362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:00.900464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:03.021342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:05.529479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:08.362950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:10.366503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:12.370174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:14.538889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.150488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:35.334234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:37.323128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:39.643593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:42.244033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:44.829379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:46.953914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:49.024002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:51.388036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:53.757271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:56.478506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:58.898492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:01.013271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:03.129610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:05.650736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:08.472168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:10.467196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:12.474224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:14.642407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.258633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:35.439173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:37.428323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:39.765935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:42.407951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:44.933461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:47.067022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:49.132380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:51.500518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:53.928632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:56.582087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:59.006287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:01.128398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:03.247468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:05.761704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:08.579175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:10.573429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:12.580366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:14.747480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.361154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:35.547648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:37.534677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:39.885996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:42.571137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:10:05.933159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:08.688079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:10.676647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:12.687141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:14.844213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.459817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:35.644386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:37.631722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:39.997472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:42.736337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:45.138250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:54.274380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:57.165304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:59.228503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:01.351819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:03.465398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:06.072581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:08.788065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:10.773627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:12.790401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:14.946965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.566332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:35.751119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:37.734566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:40.108774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:42.881836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:45.240768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:47.405503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:09:51.832166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:54.430683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:57.284315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:59.343765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:01.475346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:03.587576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:06.215015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:08.906116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:10.878454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:12.895324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:15.041642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.671110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:35.851502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:37.837403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:40.216830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:43.032062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:45.339533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-15T12:10:01.578583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:03.692913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:06.362186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:09.005564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:10.985790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:13.015489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:15.156682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:33.778538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:35.952819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:37.940663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:40.333179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:43.168228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:45.444444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:47.625529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:49.952598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:52.065451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:54.741521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:57.482946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:09:59.562444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:01.688908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:03.799918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:06.502775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:09.109740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:11.085581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T12:10:13.127906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-15T12:10:22.469150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ALTASTCholesterolGtpHDLLDLUrine proteinagedental carieseyesight(left)eyesight(right)fasting blood sugarhearing(left)hearing(right)height(cm)hemoglobinrelaxationserum creatininesmokingsystolictriglyceridewaist(cm)weight(kg)
ALT1.0000.7340.1100.623-0.2690.0900.035-0.0770.0090.0600.0650.1950.0290.0280.2660.4200.2090.2350.0000.2080.3570.4590.451
AST0.7341.0000.1010.471-0.0850.0580.0380.1060.003-0.021-0.0180.1160.0000.0000.0780.2350.1640.1580.0250.1750.1940.2500.198
Cholesterol0.1100.1011.0000.1440.1550.888-0.0040.0740.002-0.008-0.0030.0540.0450.031-0.0760.0540.0920.0190.0330.0540.2530.0750.029
Gtp0.6230.4710.1441.000-0.2260.0650.042-0.0260.0320.0450.0500.2810.0130.0000.2970.4490.2800.2930.1660.2650.4610.4680.440
HDL-0.269-0.0850.155-0.2261.000-0.061-0.0170.0190.021-0.022-0.026-0.1370.0180.017-0.231-0.275-0.104-0.2100.131-0.101-0.469-0.397-0.383
LDL0.0900.0580.8880.065-0.0611.000-0.0070.0640.014-0.008-0.0020.0100.0000.000-0.0490.0610.0530.0480.0110.0200.0910.1000.060
Urine protein0.0350.038-0.0040.042-0.017-0.0071.0000.0100.002-0.015-0.0130.0420.0180.0210.0080.0320.0350.0170.0130.0280.0190.0310.021
age-0.0770.1060.074-0.0260.0190.0640.0101.0000.121-0.334-0.3310.2110.2570.264-0.500-0.3170.050-0.1750.1800.1130.028-0.040-0.342
dental caries0.0090.0030.0020.0320.0210.0140.0020.1211.0000.0230.0240.0060.0190.0160.0840.0730.0340.0000.1070.0290.0300.0470.075
eyesight(left)0.060-0.021-0.0080.045-0.022-0.008-0.015-0.3340.0231.0000.697-0.0570.0710.0740.2410.1590.0090.1050.078-0.0350.0250.0390.175
eyesight(right)0.065-0.018-0.0030.050-0.026-0.002-0.013-0.3310.0240.6971.000-0.0590.0670.0750.2490.1670.0150.1090.087-0.0290.0270.0440.179
fasting blood sugar0.1950.1160.0540.281-0.1370.0100.0420.2110.006-0.057-0.0591.0000.0380.0460.0300.1100.1940.0770.0770.2250.2700.2610.179
hearing(left)0.0290.0000.0450.0130.0180.0000.0180.2570.0190.0710.0670.0381.0000.5170.0940.0410.0060.0180.0210.0540.0000.0270.052
hearing(right)0.0280.0000.0310.0000.0170.0000.0210.2640.0160.0740.0750.0460.5171.0000.0930.0420.0030.0210.0180.0510.0000.0170.051
height(cm)0.2660.078-0.0760.297-0.231-0.0490.008-0.5000.0840.2410.2490.0300.0940.0931.0000.5820.1220.4740.4170.0980.1690.3880.695
hemoglobin0.4200.2350.0540.449-0.2750.0610.032-0.3170.0730.1590.1670.1100.0410.0420.5821.0000.2390.4890.4040.1940.2960.4000.541
relaxation0.2090.1640.0920.280-0.1040.0530.0350.0500.0340.0090.0150.1940.0060.0030.1220.2391.0000.1010.0970.7380.2300.2900.271
serum creatinine0.2350.1580.0190.293-0.2100.0480.017-0.1750.0000.1050.1090.0770.0180.0210.4740.4890.1011.0000.0200.0820.1590.2870.419
smoking0.0000.0250.0330.1660.1310.0110.0130.1800.1070.0780.0870.0770.0210.0180.4170.4040.0970.0201.0000.0890.2390.2240.312
systolic0.2080.1750.0540.265-0.1010.0200.0280.1130.029-0.035-0.0290.2250.0540.0510.0980.1940.7380.0820.0891.0000.2150.3220.274
triglyceride0.3570.1940.2530.461-0.4690.0910.0190.0280.0300.0250.0270.2700.0000.0000.1690.2960.2300.1590.2390.2151.0000.3980.351
waist(cm)0.4590.2500.0750.468-0.3970.1000.031-0.0400.0470.0390.0440.2610.0270.0170.3880.4000.2900.2870.2240.3220.3981.0000.810
weight(kg)0.4510.1980.0290.440-0.3830.0600.021-0.3420.0750.1750.1790.1790.0520.0510.6950.5410.2710.4190.3120.2740.3510.8101.000

Missing values

2025-12-15T12:10:15.328707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-15T12:10:15.566994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageheight(cm)weight(kg)waist(cm)eyesight(left)eyesight(right)hearing(left)hearing(right)systolicrelaxationfasting blood sugarCholesteroltriglycerideHDLLDLhemoglobinUrine proteinserum creatinineASTALTGtpdental cariessmoking
0351708597.00.90.91111878972391537014219.811.06111512511
120175110110.00.70.91111979882111287111415.911.119253010
2451556586.00.90.91111080801931205711213.730.61090140027600
3451658094.00.80.71115888249210366469116.910.932363600
4201656081.01.50.11110964100179200479214.911.226281500
5601605078.01.00.9221267511417774986413.911.047237001
6401759095.00.91.01113088902073313910216.511.019221900
7401807585.01.51.5111106010017062589914.021.429203211
8401706074.01.21.511895783178696010412.920.717171400
9451555578.00.71.01111481961841774110713.110.622155600
ageheight(cm)weight(kg)waist(cm)eyesight(left)eyesight(right)hearing(left)hearing(right)systolicrelaxationfasting blood sugarCholesteroltriglycerideHDLLDLhemoglobinUrine proteinserum creatinineASTALTGtpdental cariessmoking
38974301706572.01.21.011111771001771285110014.410.822284311
38975301807585.01.51.2111237185185556710716.210.823243301
38976401656085.00.70.81114090124178152559314.731.033405400
38977551506589.00.51.0111328181183855211413.410.716101000
38978401706577.01.51.511110629118784799116.110.928433611
38979401656080.00.40.611107609314453617212.310.518182110
38980451555575.01.51.21112672912271007613112.520.623111200
3898140170105124.00.60.511141851152251964813817.110.824233511
38982401605575.01.51.5119569102206487911612.010.624201701
38983551756081.11.01.0111146686212576413713.911.018121601

Duplicate rows

Most frequently occurring

ageheight(cm)weight(kg)waist(cm)eyesight(left)eyesight(right)hearing(left)hearing(right)systolicrelaxationfasting blood sugarCholesteroltriglycerideHDLLDLhemoglobinUrine proteinserum creatinineASTALTGtpdental cariessmoking# duplicates
0201555579.51.51.5111187083149102438615.510.7324120002
1201556580.00.90.911110609013981576613.310.6121115012
2201605066.01.21.5111126397195516811714.710.8211317112
3201606074.20.91.2111307095165126558416.811.0151932012
4201606076.00.40.4111308012012436536315.111.0151116102
5201606083.01.51.51112280115179426210815.110.9141527102
6201606577.50.80.711109638916654738215.811.1272123002
7201607082.01.20.6111368993185163678516.711.0274053102
8201607088.01.21.2111107083169122598616.410.9223626012
9201607090.00.91.011110679315267518814.410.8201534002